Geological structures such as joints and faults in rock mass have significant influence on open pit mining. Hence, it is critical to develop a understanding of dynamic joint behaviour under blasting loading. This in turn can provide both theoretical and practical guidance to improve blasting rock fragmentation and associated bucket excavating efficiency. In this paper, delayed blasting on the highwall bench at Baiyunebo open-pit mine was used as an example, a nonlinear joint blasting model was also constructed. By simplifying the blasting wave propagation velocity, the P-wave normal incidence to the joint was obtained. The peak vibration velocity was 0.33m/s at 3.0s. The S-wave reflected by the joint interface was first reflected backward and then forward, which the peak vibration velocity was 0.027 m/s. By combining the relevant stress and displacement theories of type I and II cracks, the equipotential diagrams of the stress and displacement field with the vibration velocity of the particle were obtained. σx was positive in the direction of 0~330° and subjected to tensile stress, whereas σy was positive in the direction of 0~180° and under tensile stress. The longitudinal σy along joint was low in compressive stress distribution area and did not affect surrounding rock at the time. The stress concentration appeared in the lower right corner. Based on the continuous behavior of stress wave in joints, the asymmetry and continuous changes reflected in the whole process could not be analysed by the contour diagram. Hence, ANSYS used to analyse distribution of the stress field. The intensity of the shock wave after detonation was greater than that of the rock strength. Subsequently, the sub-layer shock wave supplemented the shock wave energy that was not enough to break the rock and induced further cracking. This was able to be visualized by the degree of color change post-processing. It was concluded that with the attenuation of the detonation wave energy, the stress exhibited a decreasing trend in this process. According to distribution of the peak effective stress, it was found that the peak value first increased to 10-12 MPa and then showed a downward trend. Overall, the results were validated against the finite element simulation and mathematical analysis.

To improve the productivity and efficient of modern large-scale open-cut mines, a number of technologies are developed and trialed, including new blasting equipment, larger blasting holes, high benches, air spacing, and short-delay blasting within holes. However, the relative blasting parameters need field calibration and further investigation of theories on these techniques are required. This paper studied the open-cut bench blasting at Barun Eboxi Mine of Baotou Iron and Steel Group via theoretical analysis on shock wave, numerical simulation, and field test. According to the technical conditions of the site, three sets of vertical boreholes at 310mm diameter were drilled on 24m high batter; and three sets of air-spaced charges were set up. The digital electronic detonator was used to initiate at millisecond intervals. The study found that under the condition of 24m high bench, the use of intermediate air interval is beneficial to the rock fragmentation. The delay time within the hole is 3-8ms. The bottom of the lower explosives and the top of the upper explosives were devised for initiation to optimize the initiation location. The peak effective stress points are 63.6, 52.2, and 8.9 higher, respectively. The field test of high-bench intrahole millisecond blasting in Barun Eboxi mine shows that the intrahole millisecond blasting parameters proposed in this study are feasible.

The peak particle velocity (PPV) of blasting vibration is a primary indicator to evaluate the explosion effect in an open pit mine. In the blasting scenario of an open pit mine, existing methods for predicting the peak velocity of blasting vibration are difficult to achieve ideal outcomes, leading to inappropriate designs of blasting parameters and detonation network. As a result, the peak velocity of blasting vibration cannot be accurately forecasted. Aiming at improving the prediction accuracy of blasting vibration peak velocity, based on the monitoring data of blasting vibrations at Yuanbaoshan open-pit mine with different coal storage conditions, distance from the explosive center, maximum charge per delay, elevation difference and longitudinal wave speed are chosen as the input parameters. The key parameters C and g of the support vector machine (SVM) algorithm are optimized through the global optimization of the particle swarm optimization (PSO) algorithm, so that the prediction performance of SVM is at the optimal state, and then the PSO-SVM model for predicting the peak velocity of blasting vibration is constructed. By analyzing the relationship among the input parameters and the blasting vibration peak velocity, it is concluded that the longitudinal wave speed, which represents the site conditions, is also a significant factor influencing the propagation of blasting vibration velocity. Comparing to the test results of the RS-dept and the improved Sadovsky formula, it is found that the maximum error of blasting vibration prediction by the PSO-SVM model is reduced by 48.19% and 53.6% compared with that by the RS-dept model and the improved Sadovsky formula, respectively. Also, the average error of blasting vibration prediction by the combined PSO-SVM algorithm is 3.37%, which is 29.94% and 17.86% lower than that by the improved Sadovsky formula and the RS-dept model, respectively. The predicted values of the improved PSO-SVM model match best with the measured values and the predicted results are most reliable. The proposed research method can provide theoretical guidance and practical reference for the blast design of an open pit mine.

The peak particle velocity (PPV) of blasting vibration is a primary indicator to evaluate the explosion effect in an open pit mine. In the blasting scenario of an open pit mine, existing methods for predicting the peak velocity of blasting vibration are difficult to achieve ideal outcomes, leading to inappropriate designs of blasting parameters and detonation networks. As a result, the peak velocity of blasting vibration cannot be accurately forecasted. Aiming at improving the prediction accuracy of blasting vibration peak velocity, based on the monitoring data of blasting vibrations at Yuanbaoshan open‐pit mine with different coal storage conditions, distance from the explosive center, maximum charge per delay, elevation difference, and longitudinal wave speed are chosen as the input parameters. The key parameters C and g of the support vector machine (SVM) algorithm are optimized through the global optimization of the particle swarm optimization (PSO) algorithm (the values of C and g are specified in the text), so that the prediction performance of SVM is at the optimal state, and then the PSO‐SVM model for predicting the peak velocity of blasting vibration is constructed. By analyzing the relationship among the input parameters and the blasting vibration peak velocity, it is concluded that the longitudinal wave speed, which represents the site conditions, is also a significant factor influencing the propagation of blasting vibration velocity. Comparing the test results of the RS‐dept and the improved Sadovsky formula, it is found that the maximum error of blasting vibration prediction by the PSO‐SVM model is reduced by 48.19% and 53.6% compared with that by the RS‐dept model and the improved Sadovsky formula, respectively. Also, the average error of blasting vibration prediction by the combined PSO‐SVM algorithm is 3.37%, which is 29.94% and 17.86% lower than that by the improved Sadovsky formula and the RS‐dept model, respectively. Among these, the PSO‐SVM quantitative metrics yielded r ² = 0.824, root mean square error (RMSE) = 0.383, mean absolute error (MAE) = 1.113, and mean absolute percentage error (MAPE) = 4.25%. The predicted values of SVM with PSO‐optimized hyperparameters ( C , g ) ( R ² = 0.767, MAPE = 4.25%) model match best with the measured values, and the predicted results are most reliable. The proposed research method can provide theoretical guidance and practical reference for the blast design of an open pit mine.